Course detail

Optimization of Processes and Projects

FSI-VPP-AAcad. year: 2019/2020

The course deals with the following topics: The basis of mathematical process theory. Optimal regulation. The principle of Bellman as a tool for optimization of multistage processes with a general non-linear criterion function. Optimum decision policy. Dynamic programming as a tool for creation of methods for a solution of the deterministic and stochastic decision optimization problems in discrete as well as continuous range and its computation aspects. Pontryagin maximum principle. Fuzzy regulation. Applications in practical problems solution in economical decisions and in technological process control. Optimization in project management in the stages of multicriteria projects selection into portfolio in case of a restricted resource, of resource scheduling in deterministic, stochastic and fuzzy case, of cost analysis of projects and monitoring the deviations between real and scheduled projects course.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

prof. RNDr. Ing. Miloš Šeda, Ph.D.

Department

Institute of Automation and Computer Science (ÚAI)

Offered to foreign students

Of all faculties

Learning outcomes of the course unit

Knowledge: Students will know basic principles and algorithms of methods applicable to the optimization of the deterministic, stochastic and fuzzy processes, discrete and continuous. They will be made familiar with basic principles and algorithms of methods that are appropriate to creation of decision-support systems for project management, as the tool for the identification, selection and realization of projects. Skills: Students will be able to apply the above methods to the solution of the practical problems from economic decision, problems of increasing of the reliability of technological devices, problems of automation control of technological processes and problems of project management, by using of contemporary tools of the computer science. They will be able to work with modern decision-support systems.

Prerequisites

Knowledge of the basics of mathematical analysis, algebra, theory of sets, statistics and probability.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.

Assesment methods and criteria linked to learning outcomes

Course-unit credit: Active participation in the seminars, elaboration of a given project. Examination: Written and oral.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

The aim of the course is to inform the students about creations and applications of mathematical methods for optimal control of technological and economic processes e.g. in the automation of mechanical systems, in the management of production in mechanical engineering, in project management and in optimization of information systems, using contemporary tools of computer science.

Specification of controlled education, way of implementation and compensation for absences

Attendance at seminars is required. An absence can be compensated for via solving additional problems.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Bazaraa, M, S.; Sherali, H. D.; Shetty, C. M.: Nonlinear Programming. Wiley, 2013. (EN)
Bertsekas, D. P.: Dynamic Programming and Optimal Control: Vol. I. Athena Scientific, Nashua. 2017. (EN)
Brucker, P.: Scheduling Algorithms. Springer-Verlag, Berlin, 2010. (EN)
Puterman, M. L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley-Interscience, New Jersey, 2005. (EN)

Recommended reading

Winston W.L.: Operations Research. Applications and Algorithms. Thomson - Brooks/Cole, Belmont 2004. (EN)

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme M2I-A Master's

    branch M-AIŘ , 2 year of study, summer semester, compulsory

  • Programme M2I-Z Master's

    branch M-STI , 1 year of study, summer semester, recommended course

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

prof. RNDr. Ing. Miloš Šeda, Ph.D.

Syllabus

1. Basics of mathematical processes theory. Bellman optimality principle and dynamic programming. Mitten generalization of dynamic programming.
2. Optimization of continuous decision process. Pontryagin's maximum principle.
3. Deterministic application of dynamic programming.
4. An example of the optimal fuzzy regulation and fuzzy control of technological processes.
5. Stochastic applications of dynamic programming. Controlled Markov chains.
6. Increasing reliability of technological devices.
7. Basic notions of network analysis methods, CPM method.
8. Calculation by stochastic evaluation of activities (method PERT). A comparison of the results obtained by the method PERT with the results of the simulation methods.
9. Cost analysis of a project including application of fuzzy linear programming to the solution of two-criterion time-cost problem. Heuristic methods for scheduling with resources constraints.
10. Multi-criterial projects selection. Synergistic effects and hierarchical dependencies of projects.
11. Monitoring deviations between scheduled state and real state of project. System SSD-graph.
12. Balancing production belt and assembly line.
13. Scheduling production processes.

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

prof. RNDr. Ing. Miloš Šeda, Ph.D.

Syllabus

1. Solution of dynamic programming problems in Excel and Matlab. Container loading problem.
2. Resources allocation problem. Reduction of state vector dimension.
3. Examples of process optimization by means of step-by-step approximations methods.
4. Examples of continuous processes optimization from the area of regulation and control.
5. Dynamic programming of stochastic processes. Optimization of mining plan.
6. Production control for uncertain demand. Controlled Markov chains.
7. Example of optimizing reliability of serially connected system.
8. Practical examples of graphs and networks. Implementation of the CPM method in Excel and Matlab.
9. Numerical applications of the PERT method.
10. Example of the project scheduling by fuzzy linear programming.
11. Examples of heuristic scheduling in case of constrained resources.
12. Reducing project duration.
13. Evaluation of semester projects.

Elearning

eLearning: currently opened course